! git log -1 --format="%H"

aa9dbe508f9a5c056061345264537c2844a78a74

import sys
# Install gurobi
!{sys.executable} -m pip install -i https://pypi.gurobi.com gurobipy
!{sys.executable} -m pip install plotly geopy

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
from itertools import product

import gurobipy as gp
from gurobipy import GRB, quicksum

# These are ours
import problem
import visualization

Looking in indexes: https://pypi.gurobi.com
Requirement already satisfied: gurobipy in /opt/anaconda3/lib/python3.8/site-packages (9.1.1)
Requirement already satisfied: plotly in /opt/anaconda3/lib/python3.8/site-packages (4.14.3)
Requirement already satisfied: geopy in /opt/anaconda3/lib/python3.8/site-packages (2.1.0)
Requirement already satisfied: six in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.15.0)
Requirement already satisfied: retrying>=1.3.3 in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.3.3)
Requirement already satisfied: geographiclib<2,>=1.49 in /opt/anaconda3/lib/python3.8/site-packages (from geopy) (1.50)

def normalize(i, j):
    assert i != j
    return (i, j) if i < j else (j, i)

Problem configuration

from model import Autonomax, Config

# Run on the first |C| cities (mostly for testing)
C = 41

# What scenario we will be utilizing
scenario = 0
# The number of cities in the core net
NC = 8
# 1 if the core net should be a cycle; 0 if it shoul be a path.
Z = 1

autonomax = Autonomax(
    Config(
        cities=problem.cities[:C],
        distances=problem.D[:C, :C],
        demand=problem.B[scenario][:C],
        core_city_count=NC,
        core_net_is_cycle=Z,
    )
)

Academic license - for non-commercial use only - expires 2021-05-15
Using license file /Users/sjurwold/gurobi.lic

A = autonomax.model.getA()

count = lambda d: len(d) if isinstance(d, gp.tupledict) else 1

V = sum(count(v) for v in autonomax.variables.values())
C = sum(count(c) for c in autonomax.constraints.values())

print(f'V = {V}, C = {C}')

fig, ax = plt.subplots(1, figsize=(128, 128 * C/V))
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.spy(A)


total = 0
print(f'\nCONSTRAINT SETS:')
for (name, constraint) in autonomax.constraints.items():
    constraints_in_set = count(constraint)
    print(f'{constraints_in_set:>5} | {name}')
    #plt.gcf().text(0.07, 0.69 - 0.4 * (total + 0.5 * count(constraint)) / C, name, fontsize=14)
    total += constraints_in_set
    plt.plot([0, V], [total - 0.5, total - 0.5], color='grey')
    
total = 0
print(f'\nVARIABLE SETS:')
for (name, variables) in autonomax.variables.items():
    variables_in_set = count(variables)
    print(f'{variables_in_set:>5} | {name}')
    #plt.gcf().text(0.1 + 0.8 * (total + 0.5*count(variables)) / V, 0.8, name, fontsize=14)
    total += count(variables)
    plt.plot([total - 0.5, total - 0.5], [0, C], color='grey')
    
plt.savefig('constraint-matrix.png', dpi=200)

V = 16277, C = 9597

CONSTRAINT SETS:
    1 | one_control_center
   41 | control_city_directly_connected
 1681 | reduce_control_center_symmetry
   41 | core_city_ub
    1 | cycle_or_path
   41 | disallow_core_tree
    1 | exactly_nc_core_cities
   41 | control_center_is_connected
 5740 | is_connectable
  287 | is_connected_timestep
   41 | connected_graph
   41 | conservation_of_flow
  820 | force_edge_if_flow
  820 | edge_cost_lb

VARIABLE SETS:
   41 | is_control_center
  820 | is_core_edge
   41 | is_core_city
  328 | is_connected_step
11767 | is_connectable_step
  820 | flow
  820 | abs_flow
  820 | is_sub_edge
  820 | edge_cost

autonomax.model.Params.timeLimit = 600.0
autonomax.model.optimize()

Changed value of parameter timeLimit to 600.0
   Prev: inf  Min: 0.0  Max: inf  Default: inf
Gurobi Optimizer version 9.1.1 build v9.1.1rc0 (mac64)
Thread count: 8 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 9597 rows, 16277 columns and 50307 nonzeros
Model fingerprint: 0x4cc45383
Model has 8 SOS constraints
Model has 820 general constraints
Variable types: 2460 continuous, 13817 integer (13817 binary)
Coefficient statistics:
  Matrix range     [1e+00, 2e+05]
  Objective range  [1e+00, 2e+04]
  Bounds range     [1e+00, 1e+02]
  RHS range        [7e-01, 4e+01]
Presolve removed 112 rows and 6103 columns
Presolve time: 0.16s
Presolved: 9485 rows, 10174 columns, 49895 nonzeros
Variable types: 2460 continuous, 7714 integer (7714 binary)
Found heuristic solution: objective 119321.55212

Root relaxation: objective 3.326973e+03, 4914 iterations, 0.22 seconds

    Nodes    |    Current Node    |     Objective Bounds      |     Work
 Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time

     0     0 3326.97264    0  196 119321.552 3326.97264  97.2%     -    0s
H    0     0                    92863.383829 3326.97264  96.4%     -    0s
H    0     0                    68397.560244 3326.97264  95.1%     -    0s
H    0     0                    33527.204073 3326.97264  90.1%     -    0s
     0     0 4957.57303    0  357 33527.2041 4957.57303  85.2%     -    1s
H    0     0                    33354.854381 4957.57303  85.1%     -    1s
     0     0 5673.15305    0  355 33354.8544 5673.15305  83.0%     -    1s
     0     0 5673.86859    0  358 33354.8544 5673.86859  83.0%     -    1s
     0     0 5674.07090    0  359 33354.8544 5674.07090  83.0%     -    1s
     0     0 5685.70098    0  421 33354.8544 5685.70098  83.0%     -    1s
H    0     0                    32668.531504 5685.70098  82.6%     -    1s
     0     0 5685.70098    0  396 32668.5315 5685.70098  82.6%     -    1s
     0     0 5685.70098    0  372 32668.5315 5685.70098  82.6%     -    1s
     0     0 5690.93613    0  465 32668.5315 5690.93613  82.6%     -    1s
H    0     0                    32376.599754 5690.93613  82.4%     -    1s
     0     0 5695.60139    0  382 32376.5998 5695.60139  82.4%     -    1s
     0     0 5699.10397    0  335 32376.5998 5699.10397  82.4%     -    2s
     0     0 5699.41427    0  428 32376.5998 5699.41427  82.4%     -    2s
H    0     0                    31942.820184 5699.41427  82.2%     -    2s
H    0     0                    30753.064095 5699.41427  81.5%     -    2s
     0     0 5704.51383    0  405 30753.0641 5704.51383  81.5%     -    2s
H    0     0                    30598.036353 5704.51383  81.4%     -    2s
     0     0 5704.76179    0  424 30598.0364 5704.76179  81.4%     -    2s
     0     0 5705.31890    0  435 30598.0364 5705.31890  81.4%     -    2s
H    0     0                    30523.312607 5705.31890  81.3%     -    2s
H    0     0                    30192.492805 5705.31890  81.1%     -    2s
H    0     0                    26745.750239 5705.31890  78.7%     -    2s
     0     0 5705.81456    0  413 26745.7502 5705.81456  78.7%     -    2s
H    0     0                    26253.853753 5705.81456  78.3%     -    2s
     0     0 5706.89020    0  481 26253.8538 5706.89020  78.3%     -    3s
     0     0 5706.89020    0  293 26253.8538 5706.89020  78.3%     -    3s
H    0     0                    24689.151050 5706.89020  76.9%     -    3s
H    0     0                    24656.690503 5706.89020  76.9%     -    3s
     0     2 5706.89020    0  282 24656.6905 5706.89020  76.9%     -    4s
H   31    40                    24594.048913 6044.43974  75.4%   672    4s
H   32    40                    23996.901776 6044.43974  74.8%   654    4s
    39    48 6134.31198    7  305 23996.9018 6044.43974  74.8%   612    5s
H   71    80                    23944.260186 6044.43974  74.8%   420    5s
H   71    80                    23685.153280 6044.43974  74.5%   420    5s
H  108   113                    23675.153280 6044.43974  74.5%   358    6s
H  109   113                    23671.883436 6044.43974  74.5%   356    6s
H  245   237                    22087.713678 6044.43974  72.6%   246    7s
H  247   237                    20041.548942 6044.43974  69.8%   244    7s
H  390   386                    19789.406327 6044.43974  69.5%   211    8s
H  398   386                    19644.237724 6044.43974  69.2%   209    8s
H  401   386                    19582.182248 6044.43974  69.1%   209    8s
H  530   460                    19567.695808 6044.43974  69.1%   200    9s
H  538   460                    19561.616094 6044.43974  69.1%   200    9s
H  628   574                    19547.129654 6044.43974  69.1%   192    9s
H  638   574                    19547.129645 6044.43974  69.1%   191    9s
   721   702 7636.68049   93  262 19547.1296 6044.43974  69.1%   179   10s
  1630  1443 12862.7097   83  293 19547.1296 6044.43974  69.1%   122   16s
  1638  1448 8902.00742   15  328 19547.1296 6044.43974  69.1%   121   20s
  1649  1456 11130.5301    5  507 19547.1296 6044.43974  69.1%   120   25s
  1660  1463 11846.1125  164  472 19547.1296 6044.43974  69.1%   119   30s
  1670  1470 8910.15820   72  495 19547.1296 6254.78737  68.0%   119   35s
  1674  1475 6264.91683   12  412 19547.1296 6264.91683  67.9%   180   45s
  1676  1477 6264.91683   13  474 19547.1296 6264.91683  67.9%   184   51s
  1686  1486 6264.91683   15  412 19547.1296 6264.91683  67.9%   193   56s
  1710  1500 6327.23832   17  415 19547.1296 6264.91683  67.9%   209   60s
  1767  1530 7432.55428   20  314 19547.1296 6264.91683  67.9%   232   65s
  1851  1566 7859.34346   24  331 19547.1296 6264.91683  67.9%   249   70s
H 1948  1511                    19461.681117 6264.91683  67.8%   262   74s
H 1952  1440                    19247.640840 6264.91683  67.5%   263   74s
  1955  1449 15056.8688   27  336 19247.6408 6264.91683  67.5%   264   75s
H 1980  1392                    19176.766969 6264.91683  67.3%   269   76s
H 1998  1347                    19031.929571 6264.91683  67.1%   272   77s
H 2005  1284                    19019.189479 6264.91683  67.1%   273   77s
H 2008  1226                    18998.396669 6264.91683  67.0%   273   77s
H 2012  1170                    18959.053640 6264.91683  67.0%   273   77s
H 2067  1162                    18957.038442 6264.91683  67.0%   276   79s
  2112  1171 8635.65947   32  292 18957.0384 6264.91683  67.0%   278   80s
  2315  1261 8979.31053   37  304 18957.0384 6264.91683  67.0%   289   85s
  2584  1335 9656.83072   46  230 18957.0384 6264.91683  67.0%   299   91s
  2822  1416 13333.7544   67  214 18957.0384 6264.91683  67.0%   315   96s
  3049  1496 15882.0543   95   99 18957.0384 6918.29338  63.5%   323  100s
  3226  1490 7885.01447   21  319 18957.0384 6918.29338  63.5%   328  105s
H 3291  1494                    18957.038433 6918.29338  63.5%   333  110s
H 3349  1402                    18207.038442 6918.29338  62.0%   334  110s
H 3350  1206                    16708.889286 6918.29338  58.6%   334  110s
  3518  1239 10312.3076   18  364 16708.8893 7108.04440  57.5%   345  116s
  3728  1293     cutoff   33      16708.8893 7427.81813  55.5%   343  120s
  3864  1363 8461.68687   22  352 16708.8893 7433.50232  55.5%   346  125s
  4058  1419 9772.79632   42  267 16708.8893 7433.50232  55.5%   347  130s
  4288  1495 8757.40346   23  349 16708.8893 7460.45443  55.4%   349  136s
  4434  1627 14033.2422   41  292 16708.8893 7460.45443  55.4%   356  142s
  4605  1661 14894.5702   51  299 16708.8893 7460.45443  55.4%   357  145s
  4789  1867 10260.9017   28  326 16708.8893 7626.06899  54.4%   362  151s
  4943  1922 infeasible   51      16708.8893 7831.43490  53.1%   362  155s
  5275  2165 13438.1739   23  351 16708.8893 8068.89399  51.7%   364  161s
  5493  2302     cutoff   34      16708.8893 8136.52664  51.3%   362  165s
  5876  2493 10921.9649   53  233 16708.8893 8265.74013  50.5%   365  172s
H 5991  2493                    16708.889271 8265.74013  50.5%   367  172s
  6041  2538 9661.83004   29  265 16708.8893 8265.74013  50.5%   368  177s
  6158  2662 10914.6476   31  244 16708.8893 8417.53708  49.6%   373  180s
  6374  2772 10520.2118   37  285 16708.8893 8527.96709  49.0%   375  185s
H 6444  2772                    16708.889268 8549.86104  48.8%   375  185s
  6605  2936 14498.7447   32  270 16708.8893 8582.18241  48.6%   375  191s
  6892  3046 13535.4265   44  199 16708.8893 8604.15680  48.5%   373  195s
  7120  3120 infeasible   27      16708.8893 8683.89873  48.0%   377  200s
  7345  3227     cutoff   31      16708.8893 8766.78472  47.5%   381  205s
  7577  3310 16257.9287   25  359 16708.8893 8833.92072  47.1%   385  211s
  7788  3511 10294.8033   49  298 16708.8893 8928.70141  46.6%   389  215s
  8128  3600 12053.1406   26  380 16708.8893 8952.78254  46.4%   389  220s
  8256  3725     cutoff   29      16708.8893 8957.63936  46.4%   392  226s
  8522  3817 15284.7737   33  317 16708.8893 9032.03175  45.9%   396  231s
H 8667  3817                    16708.889245 9051.59324  45.8%   397  231s
  8762  3845 11366.3337   43  202 16708.8892 9076.11523  45.7%   399  238s
  8894  4097 14347.4012   37  228 16708.8892 9084.01637  45.6%   398  245s
  9345  4350 infeasible   58      16708.8892 9121.54141  45.4%   394  252s
H 9346  4350                    16708.889239 9121.54141  45.4%   394  252s
H 9403  4350                    16708.889216 9121.54141  45.4%   396  252s
  9752  4575 9373.66345   61  373 16708.8892 9131.29273  45.4%   393  261s
H10101  4575                    16708.889202 9168.76660  45.1%   393  261s
 10149  4668 13724.3085   38  247 16708.8892 9168.76660  45.1%   393  269s
H10341  4668                    16708.889199 9194.91098  45.0%   393  269s
 10386  4870 16555.7118   35  304 16708.8892 9200.43068  44.9%   393  276s
H10762  4971                    16708.889190 9265.88664  44.5%   396  283s
H10819  4971                    16708.889169 9267.33705  44.5%   399  283s
H10928  4971                    16708.889158 9294.99627  44.4%   399  283s
H10997  5216                    16708.889131 9307.11088  44.3%   400  292s
 11419  5349 11760.3464   26  314 16708.8891 9394.64032  43.8%   398  301s
 11810  5614 12201.9446   45  307 16708.8891 9439.71911  43.5%   399  309s
H12017  5614                    16708.889116 9447.33095  43.5%   398  309s
 12299  5925 11501.5436   37  294 16708.8891 9468.07561  43.3%   399  316s
 12804  6247 12296.0497   30  307 16708.8891 9516.28431  43.0%   399  324s
H12805  6247                    16708.889113 9516.28431  43.0%   399  324s
H12868  6247                    16708.889104 9516.81299  43.0%   399  324s
H13207  6247                    16708.889059 9523.00503  43.0%   398  324s
 13323  6440 14305.0238   31  268 16708.8891 9571.43924  42.7%   398  332s
 13898  6611 10789.5383   37  300 16708.8891 9619.78195  42.4%   399  342s
H14337  6865                    16708.889031 9619.78195  42.4%   402  353s
H14416  6865                    16708.889027 9628.26212  42.4%   403  353s
H14532  6865                    16708.889013 9661.72988  42.2%   403  353s
 14958  7101 11853.7361   26  369 16708.8890 9721.91751  41.8%   401  364s
 15570  7411 15766.0762   39  263 16708.8890 9771.06937  41.5%   402  373s
H15835  7411                    16708.889002 9788.00664  41.4%   402  373s
 16220  7698     cutoff   33      16708.8890 9851.47943  41.0%   402  383s
H16327  7698                    16708.888989 9851.47943  41.0%   402  383s
 16861  7792 10077.2503   43  292 16708.8890 9888.32033  40.8%   402  397s
H17039  8076                    16708.888976 9888.32033  40.8%   400  408s
H17138  8076                    16708.888961 9917.98551  40.6%   400  408s
H17248  8076                    16708.888929 9937.89746  40.5%   400  408s
H17352  8076                    16708.888896 9956.73356  40.4%   399  408s
H17628  8279                    16708.888889 9967.75222  40.3%   402  421s
H17878  8279                    16708.888879 10006.5463  40.1%   403  421s
H18055  8279                    16708.888867 10025.5091  40.0%   405  421s
H18194  8541                    16708.888763 10058.9776  39.8%   406  433s
 18834  8815 13124.1039   24  347 16708.8888 10124.7591  39.4%   409  446s
H19514  8999                    16708.888745 10157.1341  39.2%   412  461s
H19540  8999                    16708.888732 10157.1341  39.2%   413  461s
H19941  8999                    16708.888729 10185.0608  39.0%   410  461s
 20067  9448 13097.0966   24  341 16708.8887 10207.0443  38.9%   410  475s
 20980  9954 15579.6566   36  310 16708.8887 10298.1374  38.4%   410  489s
 22015 10266 13731.5403   61   92 16708.8887 10357.3364  38.0%   409  505s
 22718 10606 14525.2078   25  316 16708.8887 10396.5626  37.8%   410  519s
 23529 10978 13327.1659   30  326 16708.8887 10460.1856  37.4%   411  535s
 24355 11372     cutoff   40      16708.8887 10514.5400  37.1%   410  551s
 25123 11803 12384.7652   83  261 16708.8887 10547.8016  36.9%   411  572s
 26051 12250 16379.7136   31  284 16708.8887 10602.7426  36.5%   411  586s
 27041 12491     cutoff   38      16708.8887 10685.5218  36.0%   409  600s

Cutting planes:
  Gomory: 34
  Cover: 1225
  Implied bound: 1
  MIR: 435
  Flow cover: 958
  GUB cover: 96
  Inf proof: 2
  Zero half: 90
  RLT: 419
  Relax-and-lift: 35

Explored 27875 nodes (11418601 simplex iterations) in 600.02 seconds
Thread count was 8 (of 8 available processors)

Solution count 10: 16708.9 16708.9 16708.9 ... 16708.9

Time limit reached
Best objective 1.670888872899e+04, best bound 1.070523302708e+04, gap 35.9309%

Solution

city_info = pd.DataFrame(autonomax.city_info())
city_info

	Index	Name	IsCoreCity	IsControlCenter	Demand	IngoingFlow	OutgoingFlow
0	0	Boden	False	False	4.5	3.999998e+00	8.499998
1	1	Borås	False	False	0.7	1.220000e+01	12.899999
2	2	Eskilstuna	True	False	1.1	1.113000e+02	112.400000
3	3	Falun	False	False	2.3	0.000000e+00	2.300000
4	4	Gävle	True	False	1.0	3.950000e+01	40.500000
5	5	Göteborg	False	False	5.8	1.918465e-13	5.800000
6	6	Halmstad	False	False	3.4	2.200000e+00	5.600000
7	7	Haparanda	False	False	4.6	0.000000e+00	4.600000
8	8	Helsingborg	False	False	2.2	0.000000e+00	2.200000
9	9	Hudiksvall	False	False	3.9	2.740000e+01	31.300000
10	10	Jönköping	False	False	1.2	1.290000e+01	14.100000
11	11	Kalmar	False	False	4.0	1.600000e+00	5.600000
12	12	Karlskrona	False	False	1.6	0.000000e+00	1.600000
13	13	Karlstad	False	False	0.9	2.629008e-13	0.900000
14	14	Kiruna	False	False	4.0	0.000000e+00	3.999999
15	15	Kristianstad	False	False	3.0	3.900000e+00	6.899999
16	16	Lidköping	False	False	1.2	7.600000e+00	8.800000
17	17	Linköping	False	False	4.1	3.910000e+01	43.200000
18	18	Luleå	False	False	2.0	1.310000e+01	15.099999
19	19	Malmö	False	False	2.6	0.000000e+00	2.600000
20	20	Motala	False	False	2.4	1.130000e+01	13.700000
21	21	Norrköping	True	False	1.7	1.046000e+02	106.300000
22	22	Nyköping	True	False	4.6	4.940000e+01	54.000000
23	23	Sandviken	True	False	4.5	3.700001e+00	8.200001
24	24	Skellefteå	False	False	4.4	1.510000e+01	19.499999
25	25	Skövde	False	False	2.5	8.800000e+00	11.299999
26	26	Stockholm	True	False	7.0	4.240000e+01	49.400001
27	27	Sundsvall	False	False	0.7	2.670000e+01	27.400000
28	28	Trelleborg	False	False	1.3	0.000000e+00	1.300000
29	29	Uddevalla	False	False	4.0	3.979039e-13	4.000000
30	30	Umeå	False	False	3.2	1.950000e+01	22.699999
31	31	Uppsala	True	False	1.9	4.050000e+01	42.400000
32	32	Varberg	False	False	0.8	5.600000e+00	6.400000
33	33	Vetlanda	False	False	2.1	9.199999e+00	11.300000
34	34	Vänersborg	False	False	3.6	4.000000e+00	7.600000
35	35	Västervik	False	False	1.8	5.600000e+00	7.400000
36	36	Västerås	True	True	1.4	1.124000e+02	1.400001
37	37	Växjö	False	False	2.3	6.899999e+00	9.199999
38	38	Örebro	False	False	4.1	8.999999e-01	5.000000
39	39	Örnsköldsvik	False	False	3.1	2.270000e+01	25.799999
40	40	Östersund	False	False	0.9	7.460699e-13	0.900000

edge_info = pd.DataFrame(autonomax.edge_info())
edge_info

	From	To	Type	Flow	Cost	Distance
0	Skellefteå	Umeå	SUB	19.499999	731.139126	111
1	Eskilstuna	Västerås	CORE	112.400000	430.000000	43
2	Gävle	Hudiksvall	SUB	-31.300000	1278.724559	118
3	Norrköping	Nyköping	CORE	-54.000000	580.000000	58
4	Nyköping	Stockholm	CORE	-49.400000	900.000000	90
5	Linköping	Norrköping	SUB	43.200000	408.715170	44
6	Boden	Kiruna	SUB	-3.999998	637.912738	291
7	Linköping	Vetlanda	SUB	-11.300000	589.291375	138
8	Sundsvall	Östersund	SUB	-0.900000	70.870179	166
9	Kalmar	Västervik	SUB	5.600000	300.208553	139
10	Halmstad	Helsingborg	SUB	-2.200000	58.849859	79
11	Umeå	Örnsköldsvik	SUB	22.699999	849.479911	111
12	Lidköping	Skövde	SUB	8.800000	105.450189	49
13	Eskilstuna	Örebro	SUB	-5.000000	129.560337	83
14	Karlstad	Örebro	SUB	0.900000	29.229967	77
15	Halmstad	Varberg	SUB	5.600000	102.802153	65
16	Lidköping	Vänersborg	SUB	-7.600000	121.696732	60
17	Motala	Skövde	SUB	-11.299999	468.037920	118
18	Uddevalla	Vänersborg	SUB	4.000000	22.172758	21
19	Gävle	Sandviken	CORE	-8.200000	250.000000	25
20	Hudiksvall	Sundsvall	SUB	-27.400000	641.652310	81
21	Sandviken	Västerås	CORE	-1.400001	1100.000000	110
22	Linköping	Motala	SUB	-13.700000	167.788674	51
23	Borås	Göteborg	SUB	-5.800000	119.727508	71
24	Jönköping	Linköping	SUB	14.100000	633.137863	125
25	Sundsvall	Örnsköldsvik	SUB	-25.799999	1177.683846	127
26	Kristianstad	Malmö	SUB	-2.600000	84.931621	94
27	Borås	Varberg	SUB	-6.400000	165.811280	84
28	Vetlanda	Växjö	SUB	-9.199999	187.740561	72
29	Kristianstad	Trelleborg	SUB	-1.300000	53.600690	104
30	Haparanda	Luleå	SUB	4.600000	210.858567	124
31	Luleå	Skellefteå	SUB	15.099999	742.410198	133
32	Stockholm	Uppsala	CORE	-42.400000	690.000000	69
33	Kristianstad	Växjö	SUB	6.899999	296.827592	120
34	Norrköping	Västervik	SUB	-7.400000	287.369522	112
35	Falun	Sandviken	SUB	2.300000	48.115171	65
36	Gävle	Uppsala	CORE	40.500000	600.000000	60
37	Kalmar	Karlskrona	SUB	-1.600000	45.527165	79
38	Borås	Jönköping	SUB	12.899999	312.907804	82
39	Boden	Luleå	SUB	8.499998	58.656830	32
40	Eskilstuna	Norrköping	CORE	-106.300000	1020.000000	102

core_cost = sum(edge_info[edge_info['Type'] == 'CORE']['Cost'])
subnet_cost = sum(edge_info[edge_info['Type'] == 'SUB']['Cost'])
total_cost = core_cost + subnet_cost

print(f'core   = {core_cost:>9.3f}')
print(f'subnet = {subnet_cost:>9.3f}')
print('------------------')
print(f'total  = {total_cost:>9.3f}')
assert abs(autonomax.model.getObjective().getValue() - total_cost) < 1e-6

core   =  5570.000
subnet = 11138.889
------------------
total  = 16708.889

visualization.show(pd.DataFrame(autonomax.city_info()), pd.DataFrame(autonomax.edge_info()))